Association of Family Support With Lower Modifiable Risk Factors for Dementia Among Cognitively Impaired Older Adults.

OBJECTIVES: Cognitive impairment poses considerable challenges among older adults, with the role of family support becoming increasingly crucial. This study examines the association of children's residential proximity and spousal presence with key modifiable risk factors for dementia in cognitively impaired older adults. METHODS: We analyzed 14,600 individuals (35,165 observations) aged 50 and older with cognitive impairment from the Health and Retirement Study (1995-2018). Family support was categorized by spousal presence and children's residential proximity. Modifiable risk factors, including smoking, depressive symptoms, and social isolation, were assessed. Associations between family support and the modifiable risk factors were determined using mixed-effects logistic regressions. RESULTS: A significant proportion of older adults with cognitive impairment lacked access to family support, with either no spouse (46.9%) or all children living over 10 miles away (25.3%). Those with less available family support, characterized by distant-residing children and the absence of a spouse, had a significantly higher percentage of smoking, depressive symptoms, and social isolation. Moreover, we revealed a consistent gradient in the percentage of the risk factors by the degree of family support. Relative to older adults with a spouse and co-resident children, those without a spouse and with all children residing further than 10 miles displayed the highest percentage of the risk factors. These findings were robust to various sensitivity analyses. CONCLUSIONS: Family support from spouses and nearby children serves as a protective factor against modifiable dementia risk factors in cognitively impaired older adults. Policies that strengthen family and social support may benefit this population.

Children's Residential Proximity, Spousal Presence and Modifiable Risk Factors for Dementia among Older Adults with Cognitive Impairment.

Background: Cognitive impairment in older adults poses considerable challenges, and the role of family support becomes increasingly crucial. This study aims to examine the impact of children's residential proximity and spousal presence on the key modifiable risk factors for dementia among older adults with cognitive impairment. Methods: Utilizing the Health and Retirement Study (HRS) data from 1995 to 2018, we analyzed 14,731 participants (35,840 person-waves) aged 50 and older with cognitive impairment. Family support was characterized based on the presence of a spouse and residential proximity to children. Smoking, depressive symptoms and social isolation were included as the key modifiable risk factors for dementia identified in later life. Using mixed-effects logistic regressions, associations between access to family support and the modifiable risk factors were determined, adjusting for various socio-demographic and health-related factors. Results: Significant associations were found between access to family support and modifiable risk factors for dementia. Cognitively impaired older adults with less available family support, characterized by distant-residing children and the absence of a spouse, had significantly higher risks of smoking, depressive symptoms, and social isolation. Moreover, we revealed a consistent gradient in the prevalence of the risk factors based on the degree of family support. Relative to older adults with a spouse and co-resident children, those without a spouse and with all children residing further than 10 miles displayed the highest risks of smoking, depressive symptoms, and social isolation. Conclusion: Access to family support, particularly from spouses and proximate children, plays a protective role against key modifiable risk factors for dementia in older adults with cognitive impairment. The findings highlight the need for bolstering family and social support systems to enhance the well-being of this vulnerable population.

COVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risk.

This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.

A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization.

In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas.

Modeling Cross-Regional Transmission and Assessing the Effectiveness of Restricting Inter-Regional Population Movements in Controlling COVID-19 - Xi'an City, Shaanxi Province, China, 2021.

Introduction: The aim of this study was to construct an assessment method for cross-regional transmission of coronavirus disease 2019 (COVID-19) and to provide recommendations for optimizing measures such as interregional population movements. Methods: Taking Xi'an City as the example subject of this study's analysis, a Cross-Regional-Gravitational-Dynamic model was constructed to simulate the epidemic in each district of Xi'an under three scenarios of controlled population movement (Scenario 1: no intensive intervention; Scenario 2: blocking Yanta District on December 18 and blocking the whole region on December 23; and Scenario 3: blocking the whole region on December 23). This study then evaluated the effects of such simulated population control measures. Results: The cumulative number of cases for the three scenarios was 8,901,425, 178, and 474, respectively, and the duration of the epidemic was 175, 18, and 22 days, respectively. The real world prevention and control measures in Xi'an reduced the cumulative number of cases for its outbreak by 99.98% in comparison to the simulated response in Scenario 1; in contrast, the simulated prevention and control strategies set in Scenarios 2 (91.26%) and 3 (76.73%) reduced cases even further than the real world measures used in Xi'an. Discussion: The constructed model can effectively simulate an outbreak across regions. Timely implementation of two-way containment and control measures in areas where spillover is likely to occur is key to stopping cross-regional transmission.

A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations.

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.